Comparative Analysis of CNN, RNN, LSTM, and Transformer Architectures in Deep Learning
Prof. Dishita Mashru · 2023
Deep learning has revolutionized numerous fields within artificial intelligence by enabling machines to learn hierarchical, complex representations of data. Among the most widely adopted architectures are Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformers. Each of these architectures offers unique capabilities and presents distinct trade-offs in performance, interpretability, and computational efficiency. This paper presents an in-depth comparative analysis of CNNs, RNNs, LSTMs, and Transformers. We explore their theoretical underpinnings, mathematical models, computational complexities, and application domains. Empirical results across several benchmark datasets—including MNIST, IMDB, and WMT English-German translation tasks—are presented along with visualizations. The comparative evaluation highlights the advantages, limitations, and real-world use cases of each model, providing guidance for model selection and potential hybrid approaches for achieving state-of-the-art performance.